Organoids are three-dimensional cell culture systems derived from pluripotent stem cells or isolated organ progenitors that differentiate to form an organ-like tissue exhibiting multiple cell types that self-organize to form a structure not unlike the organ in vivo. These unique tissues have the potential to model disease and can be used as an alternative system for drug testing that may better recapitulate effects in human patients. Thus, organoid culture represents an attractive model for personalized medicine allowing the testing of existing and experimental treatments on samples with distinct genomic individual signatures. Considering this promise of adapting organoid technology for precision medicine, we aim to combine organoid culture with quantitative proteomics to quantify global changes in protein expression, thereby identifying novel signalling pathways and targets upon defined environmental contexts.
Patients suffering from high grade Clear Cell Renal Cell Carcinoma (ccRCC) have poor survival outcome, identification of key genes and proteins involved in the initiation and progression of ccRCC could serve valuable information to extend patient survival. In this project we aim to verify a potential biomarker panel assembled from publicly available in silico datasets, using DNA gene chip and RNA-Seq data repositories. Based on 158 surgically removed and pathologically identified ccRCC tissue samples we endorsed the computationally expected 30 genes using RNA sequencing examination for gene expression analysis. Proteins encoded by genes/transcripts showing differential expression in ccRCC patients are analyzed by targeted mass spectrometry.